diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GMMExpectationMaximizationSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GMMExpectationMaximizationSuite.scala
new file mode 100644
index 0000000000000..0aaedb1911715
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GMMExpectationMaximizationSuite.scala
@@ -0,0 +1,44 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.clustering
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.linalg.{Vectors, Matrices}
+import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+
+class GMMExpectationMaximizationSuite extends FunSuite with LocalSparkContext {
+  test("single cluster") {
+    val data = sc.parallelize(Array(
+        Vectors.dense(6.0, 9.0),
+        Vectors.dense(5.0, 10.0),
+        Vectors.dense(4.0, 11.0)
+      ))
+    
+    // expectations
+    val Ew = 1.0;
+    val Emu = Vectors.dense(5.0, 10.0)
+    val Esigma = Matrices.dense(2, 2, Array(2.0 / 3.0, -2.0 / 3.0, -2.0 / 3.0, 2.0 / 3.0))
+    
+    val gmm = GMMExpectationMaximization.train(data, 1)
+    assert(gmm.w(0) ~== Ew absTol 1E-5)
+    assert(gmm.mu(0) ~== Emu absTol 1E-5)
+    assert(gmm.sigma(0) ~== Esigma absTol 1E-5)
+  }
+}